Sr Machine Learning Engineer

On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology

• Building the future of Disney’s media business: DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.

• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

Our team is responsible for developing, implementing, and maintaining Hulu's recommendation and personalization algorithms. As part of this team, you will collaborate with Engineering, Product, and Data teams to apply machine learning techniques to achieve strategic personalization goals. You will explore innovative, cutting-edge methods for recommendations and constantly seek to optimize our processes. You will play a crucial role in reshaping the future of Hulu's recommendation system by challenging the status quo and making strategic technological decisions to enhance Hulu's business outcomes.

Responsibilities:

• Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop algorithms for personalization, recommendation, and other predictive systems and bring it to large-scale real-time recommendation pipeline; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams

• Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools; deploy scalable streaming and batch data pipelines to support petabyte scale datasets

• Development Best Practices: Maintain existing and establish new algorithm development, testing, and deployment standards

• Collaborate with product and business stakeholders: Identify and define new personalization opportunities with product team and work with data teams to improve how we do data collection, experimentation and analysis

​Basic Qualifications:

• Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience

• 5+ years of experience in developing highly scalable machine learning products

• 5+ years writing production-level, scalable Python codes

• In-depth understanding of deep learning technology in recommendation system or NLP fields

• Proficiency in at least one of the following deep learning framework, tensorflow, pytorch

• Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark

• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment

• Ability to articulate the usage and behavior of models and algorithms to both technical and non-technical audiences

Preferred Qualifications:

• MS or PhD in statistics, math, computer science, or related quantitative field

• Familiarity with Java and/or Scala programming languages

• Production experience with developing content recommendation algorithms at scale and familiar with metadata management, data lineage, and principles of data governance

• Building streaming data pipelines using Kafka, Spark, or Flink

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